class_docstr: Example agent whose learning curve is a random sigmoid.
  
  The dummy hyperparameters "width" and "height" determine the slope and
  maximum reward value reached.
code: def load_checkpoint(self, checkpoint_dir):
      path = os.path.join(checkpoint_dir, "checkpoint")
      with open(path, "r") as f:
          self.timestep = json.loads(f.read())["timestep"]
  
file_path: ray/python/ray/tune/utils/mock_trainable.py
imports: import json
  import numpy
  import os
  from ray.tune import Trainable
label_desc: Use image node_id 4 for calling the MyTrainableClass obj's underlying member method code with example usage: obj.load_checkpoint(checkpoint_dir) without return types